Introduction

What is the bugRzilla Package?

BugRzilla is an R package that helps the user to interact with the Bugzilla through an API.

To learn more, see bugRzilla.

About the BugRzilla Google Summer of COde Project:-

The project bugRzilla is an issue tracker for the R-core members. The goal of the project is to help users to submit issues to R Bugzilla.

About the This Project:-

Explore the issues and bugs on the R Bugzilla to make the submission from bugRzilla better. It might help to identify useful patterns for R core or report the status of the R Bugzilla.

To learn more, see bugzilla_viz.

Setup Database on your local system

Download SQL and MySQL Workbench

To install SQL on Ubuntu one can refer a blog post by digitalocean. To install MySQL workbench on Ubuntu one can refer a blog post by linuxhint

Download R_bugzilla data

  1. The R_bugzilla data can be downloaded from link.
  2. Since the downloaded data is a zip file so make sure you unzip the file before dumping the file which will have an extension .sql (eg: R-bugs.sql).

Dump downloaded R_bugzilla to MySQL workbench.

Before one import the R_bugzilla SQL file one may need to create the (empty) database from MySQL if it doesn’t exist already and the exported SQL don’t contain CREATE DATABASE (exported with –no-create-db or -n option), before you can import it.

After considering this open your Terminal and run the command: mysqldump -u my_username -p database_name > output_file_path

The options in use are:
  1. The -u flag indicates that the MySQL username will follow.
  2. The -p flag indicates we should be prompted for the password associated with the above username. database_name is of course the exact name of the database to export.
  3. The > symbol is a Unix directive for STDOUT, which allows Unix commands to output the text results of the issued command to another location. In this case, that output location is a file path, specified by output_file_path.

bugRzilla Analysis

For the connection to the database, I’m using the dplyr package, it supports to the widely-used open source databases like MySQL.

The libraries used for the analysis:

# loading packages
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(dbplyr)
## 
## Attaching package: 'dbplyr'
## The following objects are masked from 'package:dplyr':
## 
##     ident, sql
library(RMySQL)
## Loading required package: DBI
library(DBI)
library(DT)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.1.2     ✓ stringr 1.4.0
## ✓ tidyr   1.1.3     ✓ forcats 0.5.1
## ✓ readr   1.4.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dbplyr::ident() masks dplyr::ident()
## x dplyr::lag()    masks stats::lag()
## x dbplyr::sql()   masks dplyr::sql()
library(ggplot2)
library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout

Connect bugRzilla SQL Database with R

# Connecting R with MySQL
con <- dbConnect(
    MySQL(),
    dbname='bugRzilla', # change the database name to your database name
    username='root', # change the username to your username
    password='1204', # update your password
    host='localhost',
    port=3306)

#  Accessing Tables names from the Database
DBI::dbListTables(con)
## [1] "attachments"   "bugs"          "bugs_activity" "bugs_fulltext"
## [5] "bugs_mod"      "components"    "longdescs"

Data Exploartion of Bugs Table from the Database

bugs_df <- tbl(con, "bugs")
## Warning in .local(conn, statement, ...): Decimal MySQL column 24 imported as
## numeric
## Warning in .local(conn, statement, ...): Decimal MySQL column 25 imported as
## numeric
# Converting `bugs_df` to `dataframe` and showing the `datatable`
bugs_df <- as.data.frame(bugs_df)
## Warning in .local(conn, statement, ...): Decimal MySQL column 24 imported as
## numeric
## Warning in .local(conn, statement, ...): Decimal MySQL column 25 imported as
## numeric
datatable(bugs_df, options = list(scrollX = TRUE,
                                                 pageLength = 5, lengthMenu = c(5, 10, 50, 100)))
## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html
From the above table we can conclude that the few of the columns are having wrong datatype like:
  1. creation_ts
  2. delta_ts
  3. estimated_time
  4. remaining_time
  5. deadline
Also there are columns which are empty so they are of no use of the analysis like:
  1. target_milestone
  2. qa_contact
  3. status_whiteboard

Cleaning the data

First steps, check the data and prepare it for what we want: 

bugs_df <- bugs_df %>%
    mutate_at(vars("creation_ts", "delta_ts", "lastdiffed", "deadline"), as.Date)

Visualizations

# Line plot for creation_ts, delta_ts, deadline
creation <- bugs_df$creation_ts
delta <- bugs_df$delta_ts
deadline <- bugs_df$deadline
last_diffed <- bugs_df$lastdiffed
bug_id <- bugs_df$bug_id
component_id <- bugs_df$component_id
resolution <- bugs_df$resolution


data <- data.frame(bug_id, creation)
fig <- plot_ly(data, y = ~bug_id, x = ~creation, type = 'scatter', mode = 'markers')
fig
## Warning: Ignoring 14 observations
data <- data.frame(bug_id, delta)
fig <- plot_ly(data, y = ~bug_id, x = ~delta, type = 'scatter', mode = 'markers')
fig
## Warning: Ignoring 30 observations
data <- data.frame(bug_id, deadline)
fig <- plot_ly(data, y = ~bug_id, x = ~deadline, type = 'scatter', mode = 'markers')
fig
## Warning: Ignoring 7008 observations
# bar plot with bug_id and resolution 
data <- data.frame(bug_id, resolution)
fig <- plot_ly(data, x = ~resolution, y = ~bug_id, name = 'creation', type = 'bar')
fig